Though many people are currently trying to create methods for enabling a computer to accurately determine the foreground of an image, a method that would perform such a task has proven elusive. There have been a few that have come up with solutions (See e.g., Yu and Shi, “Object-Specific Figure-Ground Segmentation”, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings, Volume 2, pages 39-45, which is hereby incorporated by reference herein in its entirety), but even those solutions aren't broad enough to solve the general problem of creating a system or method which would run effectively on any image. Even with the advancements of artificial intelligence, satisfactory solutions for having a computer automatically determine the “figure” and “ground,” according to the definitions in psychology literature or as defined by Gestalt rules of perception, until now have not been discovered. The application of encoding human perception into machine readable code has proven to be a very difficult task.
One method for having a computer represent its results for determining the foreground of an image is to direct the computer to segment out the foreground from an image. With the advancement and cost effectiveness of digital photography, many more digital images are being created than ever before. Many of these newly created digital images are taken of a person or people, whereby the person or people are arguably in the foreground of the image. Person or people segmentation from an entire image is currently a popular research topic in the field of computer vision. Most of the segmentation approaches rely heavily on training sets and accuracy of probabilistic models. Such approaches have the drawback of being computational and memory intensive. They are also sensitive to model mismatch since they are based on heavy assumptions. Some examples of model based approaches are: (1) “Efficient matching of pictorial structures,” P. F. Felzenszwalb, D. P. Huttenlocher, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 66-73, 2000; (2) “Probabilistic methods for finding people,” S. Ioffe, D. A. Forsyth, International Journal of Computer Vision, vol. 43, issue 1, pp. 45-68, 2001; (3) “Simultaneous detection and segmentation of pedestrians using top-down and bottom-up processing,” V. Sharma, J. W. Davis, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 2007; (4) “Bottom up recognition and parsing of the human body,” P. Srinivasan, J. Shi, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 2007; and (5) “Detecting and segmenting humans in crowded scenes,” M. D. Rodriguez, M. Shah, Proceedings of the 15th International Conference on Multimedia, pp. 353-356, 2007.
Rule-based systems, such as decision trees, are more popular in detection and retrieval than segmentation applications. Rule-based systems use several thresholds (hard versus soft decisions) which may pose robustness issues. However, clever choices for parameters to threshold and when the parameter threshold (early or later in the decision tree) occurs can mitigate the robustness problems. Also, hierarchical rule-based systems are not as prone to the problems that can occur in high dimensional systems with model-based approaches. Rule-based systems are more forgiving to mismatch in assumptions than model-based systems. As should be apparent, there is a long-felt and unfulfilled need to provide improved techniques for rule-based segmentation for a person or people in color images.